Drought characterization over Indian sub-continent using GRACE-based indices

Drought is a natural disaster affects water resources, agriculture, and social and economic development due to its long-term and frequent occurrence. It is crucial to characterize and monitor drought and its propagation to minimize the impact. However, spatiotemporal assessment of drought characteristics over India at the sub-basin scale based on terrestrial water storage is unexplored. In this study, the Terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment and precipitation data are used to characterize the propagation of drought. Combined Climatological Deviation Index (CCDI) and GRACE-Drought Severity Index (GRACE-DSI) were computed as CCDI utilizes both precipitation and TWSA data while GRACE-DSI uses only TWSA data. Our results showed that GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to depletion of TWS. While other sub-basins show changing trends for GRACE-DSI and CCDI. The number of sub-basins showing significant negative trends for GRACE-DSI is more than that for CCDI. Hence TWS is depleting for most of the subbasins in India. Our results show that Indo-Gangetic plains face many drought events during 2002–2004, 2009–2014 & 2015–2017. Maximum drought duration and drought severity obtained for the area of North Ladakh (not draining into Indus basins) by GRACE-DSI are 26 months (2002–2004) and − 44.2835, respectively. The maximum drought duration and drought severity obtained for the Shyok sub-basin by CCDI is 17 months (2013–2015) and − 13.4392, respectively. Monthly trend analysis revealed that 39 & 23 no. of sub-basins show significant negative GRACE-DSI trends for October and CCDI for November, respectively. At the same time, the seasonal trend shows that total 34 and 14 sub-basins exhibited a significant negative trend at post-monsoon Kharif season for both the GRACE-DSI & CCDI, respectively.


Study area and data used
Study area. This study analyses drought characteristics for entire India at sub-basin scales. The study area comprises major river basins, including Ganga, Indus, Brahmaputra, Godavari, Krishna, Cauvery, Mahanadi, Sabarmati, Narmada, Tapi, etc. We consider 90 sub-basins within the 25 river basins lying inside the Indian boundary for this study. Catchment area details of basins/sub-basins are provided in Table S1 "Supplementary material". We have used the Indian sub-basin boundaries following the Central Water Commission (CWC) and India Water Resources Information System (India-WRIS) delineation to analyze the drought characteristics 22 (Fig. 1). Refer to Table S1 ("Supplementary section") for the details of notation and naming of basins/sub-basins.

Data.
We only utilized observed data in the analysis to avoid the uncertainties imposed by the modelled data.
Further, we have not considered uncertainties caused by the interpolation, of the pointed data, to obtain gridded data.
Precipitation. For this study, we have used Indian Meteorological Department (IMD) gridded precipitation data having a spatial resolution of 0.25° × 0.25° (https:// imdpu ne. gov. in/ Clim_ Pred_ LRF_ New/ Grided_ Data_ Downl oad. html) for the duration of April 2002 to June 2017 (183 months) for the entire study area 23 . Totally 6955 rain gauge stations all over India is used to produce the IMD gridded data and inverse weighted distance interpolation technique is used to convert the gauge data into gridded data 24 . Further, rainfall records from varying number of rain gauges are used to develop the dataset with increasing gauge density over the time 25 . Although there is a varying density of rain gauges, multiple studies considered IMD precipitation as the reference data to compare global precipitation datasets 26,27 .

GRACE-Terrestrial Water Storage Anomaly (GRACE-TWSA).
We have considered GRACE monthly mass grids (RL06 mascon solutions) having a spatial resolution of 0.5° × 0.5° processed at Jet Propulsion Laboratory (JPL) for this study. GRACE dataset provides gridded monthly global water storage/height anomalies relative to timemean (time baseline from Jan 2004 to Dec 2009) 28 . The Coastal Resolution Improvement (CRI) filter reduces signal leakage errors across coastlines 29 . We have considered data from April 2002 to June 2017 due to a gap in the data for 11 consecutive months after June 2017. We apply the linear interpolation technique for missing data between April 2002 and June 2017 30 (https:// grace. jpl. nasa. gov/ data/ get-data/ jpl_ global_ masco ns/).

Methodology
In this study, we analyzed the GRACE Mascon and IMD precipitation data (Refer to "Data" for the data characteristics). Once the data is collected, we have performed monthly and seasonal trend analysis for both TWSA and IMD precipitation data at subbasin scales for the entire country to evaluate the trends in data. Regions exhibiting significant trends are identified using the Mann-Kendall test, and the magnitude of change is determined using the Theil-Sen approach (TSA). After that, we computed GRACE-DSI and CCDI index for drought characterization. We have also performed monthly and seasonal trends analysis for GRACE-DSI and CCDI index to analyze drought trends. Further we identified the drought events for both CCDI & GRACE-DSI based on the threshold www.nature.com/scientificreports/ value of − 0.5 (Table 1). Drought events are defined only if the drought indices value are less than − 0.5 continuously for greater than or equal to three months. Seasonal analysis for Post Monsoon Rabi (January to March), Pre Monsoon (April to June), Monsoon (July to September), and Post Monsoon Kharif (October to December) are done. We have considered only those drought events for which the drought index is less than − 0.5 continuously for greater than equal to three months for both the indices. We have performed this to evaluate whether drought is due to a deficit of TWSA or precipitation deficit or due to a deficit of both Precipitation and TWSA. In this study, the flow chart of the adopted methodology is shown in Fig. 2. GRACE-DSI computation. GRACE DSI utilizes only TWS data and evaluates drought events based on the deficit in TWS with respect to its mean. GRACE-DSI can be computed by standardizing TWSA (as in Eq. (1)) 17 .
where TWSA i,j represents TWSA of the year i (i = 2002 to 2017) and month j (j = 1 to 12), TWSA j is monthly mean TWSA and TWSA σ ,j is standard deviation of TWSA for month j. CCDI computation. CCDI utilizes both Precipitation and GRACE-TWSA data. It can identify the drought caused due to either deficit in Precipitation or TWS or due to partial contribution of both. For CCDI computation, we first found the Precipitation Anomaly (PA), the deviation of monthly precipitation from their mean monthly precipitation. Deviation of monthly PA from their mean monthly PA, we get Precipitation Anomaly Residual (PAR). Similarly, we have to find the Terrestrial Water Storage Anomaly Residual (TWSAR) by taking the deviation of monthly TWSA from their mean monthly TWSA. After computation of TWSAR and PAR, we have to find Combined Deviation (CD), the summation of TWSAR and PAR. By standardization of CD, we get CCDI.
where P i,j represents the amount of precipitation in year i (i = 2002 to 2017) and month j (j = 1 to 12), P µ the monthly average precipitation, and PA i,j is PA in the year i and month j, TWSA i,j represents TWSA of the year i www.nature.com/scientificreports/ and month j, TWSA j is monthly mean TWSA, PAR i,j & TWSAR i,j respectively represents PAR and TWSAR for the year i and month j, CD i,j is the combined deviation for the year i and month j, CD is monthly mean CD and CD σ is standard deviation CD for month j. Mann Kendall Test is a non-parametric test used to determine the presence or absence in a given time series data 31,32 . The null hypothesis, H 0 , states there is no monotonic trend, and this is tested against one of three possible alternatives hypothesis, H α : (1) there is a monotonic upward trend, (2) there is a monotonic downward trend, or (3) there is either a monotonic upward or a downward trend. MK test statistics can be defined as where n denotes the length of the dataset, X i and X j Represents data points in time series i and j, respectively (i < j).

Mann-Kendall Trend Analysis (MK Test).
It has been documented that for n ≥ 10, statistics S is normally distributed with where E(S) is the mean, V(S) is the variance of S, m is the number of tied groups, and t i is the size of the i-th tied group. The standard normal test statistics Z is given by A positive Z score represents an increasing trend, whereas a negative Z score represents a decreasing trend. We have considered a significance level value of 0.05 for this study.
Theil-Sen's slope estimator. Theil-Sen's approach is used to estimate the magnitude of change 33,34 . Theil-Sen's slope (β) is defined as where X i and X j Represents data points in time series i and j, respectively (i < j). Thus, a positive value of β represents an upward trend, whereas a negative value of β represents a downward trend.

Drought duration and drought severity. Drought duration is the period where the drought index value
is below the fixed threshold value (for this study, we have taken a threshold value of − 0.5). Drought Severity is the cumulative value of the drought index within the drought duration. The drought duration and drought severity of each event computed for GRACE-DSI and CCDI are listed in Tables S2 and S3 in the "Supplementary Material".

Results
After computing the GRACE-DSI and CCDI, drought events obtained from the GRACE-DSI and CCDI are highlighted and presented in "GRACE-DSI identified drought event analysis" and "CCDI identified drought event analysis" alongside the precipitation anomaly of the respective sub-basins. The spatio-temporal trend pattern of the drought indices at the monthly and seasonal windows is shown in "Monthly and seasonal trend analysis".

GRACE-DSI identified drought event analysis.
To study the drought characteristics over Indian subbasins, we have plotted drought events identified by GRACE-DSI and CCDI indices for 90 subbasins as shown in Fig. 3. A dry spell continuing for three or more months is only considered for drought characterization. Any intermediate wet spell during this dry spell continuing three or fewer months is considered a temporary anomaly within this persisting dry streak.
GRACE-DSI utilizes only precipitation data and evaluates drought events based on the deficit in TWS. Our results show that among Indus sub-basins, Indus upper sub-basin (Fig. 3a, 1c) Table S3). We also provide the drought events plots for all sub-basins in Supplementary Fig. S3. www.nature.com/scientificreports/ CCDI identified drought event analysis. CCDI utilizes both precipitation and GRACE-TWSA data. It can identify the drought caused due to either deficit in precipitation or TWS or due to partial contribution of both as shown in Fig. 3. The Indus upper sub-basin (1c) shows a maximum of four drought events (Fig. 3a) Fig. 3b that drought events DE1, DE2, and DE3 occur majorly due to deficit in precipitation, in addition there is no GRACE-DSI characterized drought events during that period. Although Indus upper and Shyok sub-basins are adjacent to each other, maximum drought duration and severity with respect to GRACE-DSI and CCDI have contrasting characteristics. This necessitates the importance of downscaling of the drought characterization to sub-basin level and application of multiple drought indices with difference properties. Brown bands are obtained for drought due to both deficits in precipitation and TWS. The Ramganga sub-basin (2b) shows two drought events (Fig. 3c) Table S2).

Monthly and seasonal trend analysis.
Monthly trend analysis shows that most of the sub-basins of Ganga and Indus have high significant negative GRACE-DSI trends for July, August, and September months (Fig. 4a,c,e, respectively). Some sub basins of Godavari, Narmada, Sabarmati, and Tapi show significant positive GRACE-DSI trends in July (Fig. 4a). While only a few sub-basins of Ganga and Indus show significant negative CCDI trends for July, August, and September months (Fig. 4b,d,f) respectively). Five sub-basins of Ganga show significant positive CCDI trends in July (Fig. 4b). Other sub-basins show either non-significant positive or negative trends for CCDI and GRACE-DSI. Some of the sub-basins that show significant negative GRACE-DSI trends and significant positive CCDI trends conclude that sub-basin that get sufficient rainfall does not show negative CCDI trends. But for, the sub-basins, which has significant negative trends for both CCDI and GRACE-DSI, depicts that the sub-basin is getting low or no rainfall, due to which TWS is continuously depleting. CCDI monthly trends for most sub-basins are showing changing trends. Monthly and seasonal CCDI and GRACE-DSI trends for all months are provided in Supplementary Figs. S1 and S2, respectively. GRACE-DSI's seasonal trends show continuous negative trends for sub-basins of Ganga and Indus throughout the year, while other sub-basins show changing trends ( Supplementary Figs. S1 & S2). Sub-basins of Indus and northern sub-basins of Ganga exhibit significant negative GRACE-DSI trends, whereas most of the sub-basins the peninsular river basins show significant positive GRACE-DSI trends for pre-monsoon season (Fig. 5a). This contrasting trend behaviour of the sub-basins in the north and south India need to be examined. The remaining sub-basins show either non-significant positive or negative trends for the pre-monsoon season. However, for CCDI, most sub-basins has either non-significant positive or negative trends for the pre-monsoon season (Fig. 5b). Further for monsoon season, sub-basins of Ganga and Indus exhibit high significant negative GRACE-DSI trends (Fig. 5c). At the same time, only a few sub-basins of Ganga show significant negative CCDI trends for the monsoon season (Fig. 5d). Interestingly, other sub-basins has either non-significant positive or negative trends for both GRACE-DSI and CCDI during monsoon season. www.nature.com/scientificreports/ In Fig. 6, we plot no. of sub-basin showing significant negative or positive trends for GRACE-DSI and CCDI. From Fig. 6, we can observe that most sub-basin shows significant negative trends for GRACE-DSI and CCDI except premonsoon, where GRACE-DSI exhibit both the positive and negative trends. This is mainly because of the higher number of sub-basins in the peninsular India that displayed positive trends during premonsoon. The number of sub-basins showing significant negative trends for GRACE-DSI is also more than that for CCDI. This difference in the characteristics of GRACE-DSI and CCDI may also due to the depletion in TWS in most of the sub-basins for the study period.

Discussion
In the past, India faces many droughts, which affects millions of people and the country's socio-economic growth. In this study, we have found the drought events over Indian sub-basins using GRACE-DSI and CCDI indices. Our results are consistent with the historic drought of 2000-2003, 2009-2010, 2015-2018 2,35 . GRACE-DSI shows high drought duration events with high severity for most sub-basins, which signifies that terrestrial water storage is depleting 5 . We have compared drought events of both CCDI and GRACE-DSI; we come to know GRACE-DSI characterized drought events are due to the deficit in TWS only. However, when CCDI only shows drought events, it concludes that it is mainly due to the precipitation deficit. But when both CCDI and GRACE-DSI show drought events, it is due to the partial contribution of both deficits in precipitation and TWS. In this study, we also noted that the Indo-Gangetic plain faces many drought events, as verified by the results 3,36 . Rodell et al. 37 Figure 5. Seasonal CCDI/GRACE-DSI trends using the Mann Kendall trend test and Theil-Sen's slope estimator over major Indian river basins from the Monsoon season. (a) GRACE-DSI trends for pre-monsoon season, (b) CCDI trends for pre-monsoon season, (c) GRACE-DSI trends for monsoon season, and (d) CCDI trends for monsoon season, respectively. Colormap over the basin represents the slope obtained from TSA, △ represents a significant increasing trend, and ▽ indicates a significant decreasing trend based on the Mann-Kendall test.  19 , which is also explained through GGDI and GRACE-DSI. In their study, Sinha et al. 18 find that the drought events of 2002 and 2004 are noteworthy in peninsular, west-central, and north-western India. In contrast, the drought event of 2010 is noticeable over central, west-central, and north-western India. During 2016-2018, Southern India faced a severe water crisis 39 , that can also be observed in our study. Mishra 35 reveals that the drought of the year 2015-2018 affected surface water and groundwater availability in the southern part of India. The meteorological drought of 2015-2018 has the most prolonged drought duration of 41 months. The study further stated that rapid depletion of groundwater and long-term drought could lead to severe problems of water availability in India. The quick deficit in groundwater in north India makes the region vulnerable to drought impacts 40 . Satish Kumar et al. 20 use six drought indices and found that CCDI and GRACE-DSI effectively study drought characteristics. Overall, the CCDI and GRACE-DSI show promising results. When we work on a large scale, both GRACE-DSI and CCDI correlate very much because working on a large scale averages the effect of precipitation. GRACE-based drought indices are very helpful in characterizing the drought in the absence of ground station data. Also, the accuracy of GRACE data is high. Further CCDI includes all aspects of drought occurrences like Meteorological, Agricultural, Hydrological and Anthropogenic activities. Although there are some limitations, like GRACE-data sets only available from April 2002 and GRACE data spatial resolution is also relatively low. Errors in precipitation and TWSA can lead to CCDI or GRACE-DSI index errors. Evaluation of the hydroclimatoligical teleconnections 41,42 linked with the drought events could be the possible future trajectory of this study.

Conclusion
In this study, during 2002-2017, the drought characteristics were examined and evaluated using the GRACE-DSI and CCDI as a metric over major river sub-basins in India. The key findings from this study are: 1. GRACE-DSI shows significant negative trends over most of the Indian sub-basins relative to CCDI, indicating that most of the drought events are due to depletion of TWS. 2. The maximum drought duration and drought severity computed by CCDI is 17 months and − 13.4392, respectively for Shyok subbasin.